RETRACTED: BMADSN: Big data multi-community anomaly detection in social networks

Hc, Manjunatha; Mohanasundaram, R.

doi:10.1177/0020720919891065

Cited by 6 publications

(1 citation statement)

References 48 publications

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“…According to [14], VAD systems are either manually built by experts setting thresholds on data or constructed automatically by learning from the available data through Machine Learning (ML). VAD is widely used in many applications such as fraud detection [15], [16], image processing [17], [18], sensor networks [19], [20], medical health [21], [22], intrusion detection [23], IT security [24], [25], [26], and social media [27], [28]. Fig.…”

Section: Video Anomaly Detectionmentioning

confidence: 99%

A Review on Video Anomaly Detection Datasets

Yossef,

Gamal,

Abdel-Kader

et al. 2023

Suez Canal Engineering, Energy and Environmental Science

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In recent years, Video Anomaly Detection (VAD) has received a lot of attention and has become a popular research topic. This is due to their immense potential in a variety of fields, including healthcare monitoring, surveillance/crowd analysis, sports, Ambient Assistive Living (AAL), event analysis, and security. Manually detecting and analysing improper behavior was a hard process, particularly in real-time scenarios, resulting in a high demand for smart surveillance systems. Moreover, the availability of data plays a vital role in training and evaluating models. Datasets in VAD are typically composed of sequences of frames or videos, some of which depict normal activities and others that depict anomalous or unusual events. These datasets provide a rich resource that encapsulates everyday routine actions alongside irregular or unusual events, fostering the development and assessment of robust anomaly detection models. This paper provides an extensive review of the most popular and recent datasets in VAD including an extensive comparison between them.

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Section: Video Anomaly Detectionmentioning

confidence: 99%

A Review on Video Anomaly Detection Datasets

Yossef,

Gamal,

Abdel-Kader

et al. 2023

Suez Canal Engineering, Energy and Environmental Science

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Co-Membership-based Generic Anomalous Communities Detection

Lapid

Kagan

Fire

2023

Neural Process Lett

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Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices or the topological structure of communities. In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC ), a novel and generic method that utilizes the information of vertices co-membership in multiple communities. CMMAC is domain-free and almost unaffected by communities’ sizes and densities. Specifically, we train a classifier to predict the probability of each vertex in a community being a member of the community. We then rank the communities by the aggregated membership probabilities of each community’s vertices. The lowest-ranked communities are considered to be anomalous. Furthermore, we present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities to facilitate research in the field. We utilized it to generate two datasets, composed of thousands of labeled anomaly-infused networks, and published them. We experimented extensively on thousands of simulated, and real-world networks, infused with artificial anomalies. CMMAC outperformed other existing methods in a range of settings. Additionally, we demonstrated that CMMAC can identify abnormal communities in real-world unlabeled networks in different domains, such as Reddit and Wikipedia.

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A survey on social network’s anomalous behavior detection

Xing,

Li,

Zhang

et al. 2024

Complex Intell. Syst.

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The onset of Web 3.0 has catalyzed the rapid advancement of social networking, transforming platforms into essential elements deeply embedded within the fabric of daily life. Researchers have proposed several methods for detecting anomalous behaviors in various scenarios. This article provides a comprehensive review of current research and the latest developments in anomalous behavior detection within social networks. We present a hierarchical three-layer categorization scheme based on the distinct characteristics of base-level detection technologies and various datasets. First, anomaly detection based on user behavioral characteristics can intuitively reflect deviations in individual behavior. However, it may overlook the overall network structure’s impact. Second, detecting anomalies within a network’s topological structure highlights structural significance, but may overlook the subtle nuances of individual behavior. Finally, the coordinated fusion method, which blends individual behavioral characteristics and the network’s topological structure, addresses the multifaceted nature of anomalies, yielding a more thorough and accurate anomaly detection strategy. This paper provides an overview and assesses the performance of three anomaly detection methods. Furthermore, we explore the challenges associated with social network anomaly detection and the potential pathways for further research.

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RETRACTED: BMADSN: Big data multi-community anomaly detection in social networks

Cited by 6 publications

References 48 publications

A Review on Video Anomaly Detection Datasets

A Review on Video Anomaly Detection Datasets

Co-Membership-based Generic Anomalous Communities Detection

A survey on social network’s anomalous behavior detection

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